Figures for Paper

R Markdown

Reading in Data

scaled_sum <- read.csv("scaled_sum.csv")
hhs_all_complete <- read_csv("hhs_all_complete.csv")

## New names:
## * `` -> ...1

## Rows: 19745 Columns: 237

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (81): submissionid, 44_meeting_attendance, 45_leadership_position, 48_...
## dbl  (155): ...1, level4_id, lat, lon, ma_area, 9_region_member, 10_mpa_impo...
## dttm   (1): updatedat

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

scaled_km_df <- read_csv("scaled_km_df.csv")

## New names:
## * `` -> ...1

## Rows: 4451 Columns: 130

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (37): submissionid, country, level1_name, level2_name, level4_name, ma_n...
## dbl (93): ...1, x1, level4_id, lat, lon, ma_area, x9_region_member, x44_meet...

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

We want to include a point where all responses are “neutral” on our map of compliance behaviors

neutral_point <- scaled_sum %>% 
filter(country == "NEUTRAL")

1. Mapping Indices

We now have one (x,y) point for each fisher observation, and can map them onto an X-Y plane of Agreement and Engagement.

  ggplot(data = scaled_sum,
                      aes(x = agree_mean, y = eng_mean)) +
   geom_point(color = "darkblue", size = 0.7, alpha = 0.4) +
  geom_point(data = neutral_point, aes(x = agree_mean, y = eng_mean),
             size = 8, color = "pink2") +
  labs( x = "Agreement (Scaled across 5 Questions)",
      y = "Engagement (Scaled across 5 Questions)",
      title = "Engagement and Agreement Index Mapping") +
#  scale_color_viridis(discrete = TRUE, option = "D") +
    # scale_x_continuous(limits = c(-3, 1.7),
    #                   breaks = c(-2, -1, 0, 1, 1.5)) +
   # scale_y_continuous(limits = c(-1.5, 1.5),
   #                   breaks = c( -1, 0, 1, 1.5)) +
  guides(colour = guide_legend(override.aes = list(size=7))) +
  theme_bw() 

*Figure 1: A plot with the **neutral* fisher reported within the spread of our Z-score-based indices. We see that Ms. Neutral is 1 SD below the mean in agreement. This location means that the majority of fishers at least somewhat agree with rules in place. Meanwhile, we see dense grouping between -0.5 and 0.5 on both axes. Quite a few fishers have the maximum score for agreement, while a smaller number meet the ceiling of engagement.

Between Cluster 1 and 4 (our “average” fishers,) we see that C4 is a full sd higher on question 43 “Do you think there is any benefit to regulating fishing via a managed access area and a reserve area?”

Both C1 and C4 (average fishers) have higher values in question x61g (willingness to change fishing behavior for the sake of conservation,) and they slightly disagree with “encouraging others to follow regulations.”

Average fishers aren’t engaging much (including meeting attendance.) From an NGO perspective, if one wants to shift fisher behavior+attitudes, then try and bring the reluctant folks into the governance process.

2. Compliance Behaviors of Admitted rule-breakers

counts_63 <- scaled_sum %>% 
  count(x63_fishing_in_reserve, country)


filtered_63 <- scaled_sum %>% 
  filter(x63_fishing_in_reserve == "1")

ggplot(data = filtered_63,
                      aes(x = agree_mean, y = eng_mean)) +
  geom_point(color = "darkblue", size = 1.2, alpha = 1,
             show.legend = TRUE) +
labs( x = "Agreement (Scaled across 5 Questions)",
      y = "Engagement (Scaled across 5 Questions)",
      title = "Engange and Agreement of Admitted rule-breakers") +
  scale_x_continuous(limits = c(-3, 2),
                     breaks = c(-2, -1, 0, 1, 2)) +
   scale_y_continuous(limits = c(-1.5, 2),
                     breaks = c( -1, 0, 1, 2)) +
  theme_bw() 

Figure 4: A graph of Agreement and Engagement measures, colored by responses of “Do you fish inside the reserve?” These fishers range

Question 63 asks if fishers break the rules, and fish in the reserve. This would be a classic measure of compliance (although self-reporting likely leads to a lower N of admitted non-compliers)

138 fishers admitted that they fish in the reserve, while 2800 said they do not. The question we’re left with is: What is different about folks who admit to breaking rules? These “Yes” rule-breakers are spread relatively evenly across all countries. Maybe they don’t think there are sanctions, their community doesn’t care about it

I’ll do the same “rule-breaker” analysis, but against the question, “Out of 10 people in your community, how many people…Fished in the reserve in the last month”

3. Full Cluster Map

Here, I’ve attached the summary statistics of the Clusters. We ahve them on hand for tables, individual stats, Chi-Squared tests, etc.

cluster_summary_df <- scaled_km_df %>% 
  group_by(cluster_no) %>% 
  summarise(eng_mean = mean(eng_mean),
            agree_mean  = mean(agree_mean),
            x44_mean = mean(x44_meeting_attendance),
            x45_mean = mean(x45_leadership_position),
            x48_mean = mean(x48_enforcement_participation),
            x53_mean = mean(x53_encourage_regulations),
            x61g_mean = mean(x61g_fishing_change_behavior), 
            x43_mean =  mean(x43_ma_benefits),
            x46_mean = mean(x46_represent_interests),
           x52_mean = mean(x52_ma_benefit_5yrs),
           x61a_mean = mean(x61a_current_regulations),
           x61f_mean = mean(x61f_rights_distribution_fair),
           x30a_mean = mean(x30_trust_local_decision),
           x44_median = median(x44_meeting_attendance),
            x45_median = median(x45_leadership_position),
            x48_median = median(x48_enforcement_participation),
            x53_median = median(x53_encourage_regulations),
            x61g_median = median(x61g_fishing_change_behavior), 
            x43_median =  median(x43_ma_benefits),
            x46_median = median(x46_represent_interests),
           x52_median = median(x52_ma_benefit_5yrs),
           x61a_median = median(x61a_current_regulations),
           x61f_median = median(x61f_rights_distribution_fair),
            x30a_median = median(x30_trust_local_decision)) %>% 
  mutate(across(where(is.numeric), ~ round(., 2)))
## Here, the last line of code rounds to the second decimal 

cluster_medians <- cluster_summary_df %>% 
  select(1, 14:23) 

cl_mean_graphable_df <- cluster_summary_df %>% 
  select(eng_mean, agree_mean, cluster_no)



scaled_cluster_counts <- scaled_km_df %>% 
  count(cluster_no)


ggplot() +
  geom_point(data = scaled_km_df,
       aes(x = agree_mean, y = eng_mean,
           color = cluster_no),
           size = 0.7, alpha = 0.7) +
   geom_point(data = cl_mean_graphable_df,
             aes(x = agree_mean, y = eng_mean,
          color = cluster_no),
           size = 6, shape = "triangle", show.legend = FALSE) +
  labs( x = "Agreement (Scaled across 5 Questions)",
      y = "Engagement (Scaled across 5 Questions)",
      title = "Mapping Clusters",
      color = "Cluster") +
  scale_x_continuous(limits = c(-3, 2),
                     breaks = c(-2, -1, 0, 1, 2)) +
   scale_y_continuous(limits = c(-1.5, 2),
                     breaks = c( -1, 0, 1, 2)) +
   guides(colour = guide_legend(override.aes = list(size=7)))

## Warning: Removed 10 rows containing missing values (geom_point).

The 4 Clusters, done marked by color. Means are marked by triangles.

Between Cluster 1 and 4 (our “average” fishers,) we see that C4 is a full sd higher on question 43 “Do you think there is any benefit to regulating fishing via a managed access area and a reserve area?”

Both C1 and C4 (average fishers) have higher values in question x61g (willingness to change fishing behavior for the sake of conservation,) and they slightly disagree with “encouraging others to follow regulations.”

Average fishers aren’t engaging much (including meeting attendance.) From an NGO perspective, if one wants to shift fisher behavior+attitudes, then try and bring the reluctant folks into the governance process.

kmeans_kable <- head(cluster_summary_df) %>% 
kable( format = "html", caption = "Summary of Clusters") %>%
   kable_styling(bootstrap_options = "striped",
                full_width = F,
                position = "left") %>%
collapse_rows(columns = 3, valign = "middle")


kmeans_kable

Summary of Clusters

cluster_no	eng_mean	agree_mean	x44_mean	x45_mean	x48_mean	x53_mean	x61g_mean	x43_mean	x46_mean	x52_mean	x61a_mean	x61f_mean	x30a_mean	x44_median	x45_median	x48_median	x53_median	x61g_median	x43_median	x46_median	x52_median	x61a_median	x61f_median	x30a_median
Committed	0.87	0.30	0.89	0.52	0.50	0.66	4.19	0.65	0.89	0.72	4.06	4.10	4.10	1	1	1	1	4	1	1	1	4	4	4
Resistant	-0.42	-0.87	-0.47	-0.65	-0.74	-0.45	3.38	0.46	0.01	0.19	2.76	2.80	2.74	-1	-1	-1	0	4	0	0	0	3	3	3
Supportive	-0.18	0.31	-0.06	-0.81	-0.86	-0.15	4.07	1.00	0.76	0.66	4.10	3.95	3.90	-1	-1	-1	0	4	1	1	1	4	4	4
Wavering	-0.24	-0.13	-0.19	-0.86	-0.87	-0.18	4.03	0.00	0.70	0.30	4.13	3.87	4.04	-1	-1	-1	0	4	0	1	0	4	4	4

4. Perceived Catch + Clusters

no_na_19 <- scaled_km_df %>% 
    drop_na(x19_current_fish_catch) %>% 
 mutate(x19_current_fish_catch = recode_factor(x19_current_fish_catch, 
    "Declined a lot" = "1. Declined a lot",
          "Declined slightly" = "2. Declined slightly",
           "Stayed the same" = "3. Stayed the same",
          "Improved slightly" = "4. Improved slightly",
          "Improved heavily" = "5. Improved heavily")) 
  

       
         
         
mean_19_df <- no_na_19 %>% 
        group_by(x19_current_fish_catch) %>% 
        summarise(eng_mean = mean(eng_mean),
                  agree_mean  = mean(agree_mean)) 

Another Version of Perceived Catch

ggplot(no_na_19, 
       aes(x = agree_mean, y = eng_mean, color = x19_current_fish_catch)) +
  geom_point(size = 0.7) +
  labs( x = "Agreement (Scaled across 5 Questions)",
      y = "Engagement (Scaled across 5 Questions)",
      title = "Perceived Change in Catch", 
      color = "Change in Fish Catch (perceived)", 
      caption = "Hypothesis: fish decline = Less Agreeing") +
  scale_x_continuous(limits = c(-3, 2),
                     breaks = c(-2, -1, 0, 1, 2)) +
   scale_y_continuous(limits = c(-1.5, 2),
                     breaks = c( -1, 0, 1, 2)) +
  guides(colour = guide_legend(override.aes = list(size=7))) 

## Warning: Removed 10 rows containing missing values (geom_point).

x19_counts <- no_na_19 %>% 
  count(x19_current_fish_catch)

x19_cluster_counts <- no_na_19 %>% 
  group_by(cluster_no) %>% 
  count(x19_current_fish_catch)


ggplot(x19_cluster_counts,
       aes(x = x19_current_fish_catch, y = n, fill = cluster_no,
           )) +
  geom_bar(position="fill", stat="identity") +
  labs( x = "Perception of Fish Stock (over last 2 years)",
      y = "Percentage",
      fill = "Cluster") +
    guides(colour = guide_legend(override.aes = list(size=7))) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

reversed_x19_counts <- 
  no_na_19 %>% 
  group_by(x19_current_fish_catch) %>% 
  count(cluster_no)

ggplot(reversed_x19_counts,
       aes(x = cluster_no, y = n, fill = x19_current_fish_catch)) +
  geom_bar(position="fill", stat="identity", alpha = 0.7) +
  labs( x = "Clusters",
      y = "Percentage of Cluster",
      title = "Perceived Catch and Clusters", 
      fill = "Perceived Change in Fish Catch \n (compared with 2 years prior)") +
  scale_fill_viridis(discrete = T) +
  theme_bw()

Here’s a breakdown of the ecology question:

Cluster 1 saw mostly declines (466/677) Cluster 2 has the most who saw slight improvements Cluster 3

I just want to check the counts of x19 by country

x19_by_country <- no_na_19 %>% 
  group_by(country) %>% 
  count(x19_current_fish_catch)

ggplot(x19_by_country) +
  geom_bar( aes(x = country, y=n, fill = x19_current_fish_catch), stat="identity", alpha=0.8) +
  theme_bw() +
  labs(fill = "Perceived Fish Catch")

x19_sans_phl <- no_na_19 %>% 
  filter(country != "PHL") %>% 
    group_by(country) %>% 
  count(x19_current_fish_catch)

ggplot(x19_sans_phl) +
  geom_bar( aes(x = country, y=n, fill = x19_current_fish_catch), stat="identity", alpha=0.8) +
  theme_bw() +
  labs(fill = "Perceived Fish Catch")

5. Fishing Effort in “Low Season”

First, I clean the dataframe

fishing_effort_17 <- scaled_km_df %>% 
  filter(x17_fishing_low_profit %in% c("Once or never",
                                         "A few times",
                                         "A few times per month",
                                        "1-2 per week",
                                         "More than 1-2 times per week",
                                       na.rm = FALSE)) %>% 
  drop_na(x17_fishing_low_profit) %>% 
mutate(x17_fishing_low_profit = recode_factor(x17_fishing_low_profit, "Once or never" = "Rarely")) %>% 
  mutate(x17_fishing_low_profit = recode_factor(x17_fishing_low_profit, "A few times" = "Rarely"))
  

x17_counts <- fishing_effort_17 %>% 
  group_by(x17_fishing_low_profit) %>% 
  count()

x17_cluster_counts <- fishing_effort_17 %>% 
  group_by(cluster_no) %>% 
  count(x17_fishing_low_profit)

Then I visualize

ggplot(data = fishing_effort_17,  aes(x = agree_mean, y = eng_mean)) +
   geom_point(aes(color = x17_fishing_low_profit), size = 0.7, alpha = 0.7) +
  labs( x = "Agreement (Scaled across 5 Questions)",
      y = "Engagement (Scaled across 5 Questions)",
      title = "Engagement and Agreement Index Mapping") +
#  scale_color_viridis(discrete = TRUE, option = "D") +
    # scale_x_continuous(limits = c(-3, 1.7),
    #                   breaks = c(-2, -1, 0, 1, 1.5)) +
   # scale_y_continuous(limits = c(-1.5, 1.5),
   #                   breaks = c( -1, 0, 1, 1.5)) +
  guides(colour = guide_legend(override.aes = list(size=7))) +
  theme_bw()

ggplot(x17_cluster_counts,
       aes(x = cluster_no, y = n, fill = x17_fishing_low_profit)) +
  geom_bar(position="dodge", stat="identity", alpha = 0.8) +
  labs( x = "Clusters (1 and 4 are 'average')",
      y = "Total # of Fishers",
      title = "Fishing Effort in Offseason", 
      fill = "Fishing Effort (Per Week)", 
      caption = "Hypothesis: resistant fishers fish more") +
  geom_text(aes(label = n),position = position_dodge(0.9), vjust = 2, size = 2, color = "gray1")+
  theme_bw()

• Resistant fishers who fish 1+ times per week: 236/526 = 45%
• Wavering fishers who fisher 1+ times per week: 257/998 = 26%
• Supportive fishers who fisher 1+ times per week: 370/1279 = 29%
• Committed fishers who fisher 1+ times per week: 236/812 = 29%

Second figure will be another stacked barplot This is rad! I’m going to code away at x18, and then do a little more interpreting.

6. Fishing Effort in “High Season”

Dataframe cleaning

x18_fishing_effort <- scaled_km_df %>% 
  filter(x18_fishing_high_profit %in% c(
    "Once or never",
"A few times",
"A few times per month",
"1-2 per week", 
"More than 1-2 times per week")) %>% 
mutate(x18_fishing_high_profit = recode_factor(x18_fishing_high_profit, "Once or never" = "Rarely")) %>% 
  mutate(x18_fishing_high_profit = recode_factor(x18_fishing_high_profit, "A few times" = "Rarely")) %>% 
  drop_na(x18_fishing_high_profit)

# basic counts

x18_counts <- x18_fishing_effort %>% 
  count(x18_fishing_high_profit)

x18_cluster_counts <- x18_fishing_effort %>% 
  group_by(cluster_no) %>% 
  count(x18_fishing_high_profit)

Let’s do the same visualization for 18, as I did for 17.

Visualziation

ggplot(x18_cluster_counts,
       aes(x = cluster_no, y = n, fill = x18_fishing_high_profit)) +
  geom_bar(position="dodge", stat="identity", alpha = 0.8) +
  labs( x = "Clusters (1 and 4 are 'average')",
      y = "Total # of Fishers",
      title = "Fishing in High Season", 
      fill = "Frequency of Fishing", 
      caption = "Hypothesis: Angry fishers fish alot") +
  geom_text(aes(label = n),position = position_dodge(0.9), vjust = 2, size = 2, color = "gray1")+
  theme_bw()

• Resistant fishers who fish 1+ times per week: 70%
• Wavering fishers who fisher 1+ times per week: 84%
• Supportive fishers who fisher 1+ times per week: 83%
• Committed fishers who fisher 1+ times per week: 73%

supportive : 719, wavering 563, Committed 414, resistant 227

7. Food Security

• Question #23: “Do you believe that the job of a fisher is secure in the future?”
  
• Question #29: “To cover family needs your household income is…”
• Question #54: “How do you rate the last year in terms of food availability?”
• Question #59: “Are you confident that you will be able to procure enough food for you and your family for the next 12 months?”
• Question #60: In the last 12 months, how often did your household eat fish?

Starting with 55 and 56

food_security <- scaled_km_df %>% 
  select(country, cluster_no, x23_job_secure, x26_fishing_income_save, x28_buyer_loans, x29_family_income, x55_worry_food, x59_food_procurement, x60_hh_fish_consumption, eng_mean, agree_mean, x54_food_availability) %>% 
  drop_na()

Ability to cover family costs

income_counts <- food_security %>% 
  group_by(cluster_no) %>% 
  count(x29_family_income)


ggplot(income_counts,
       aes(x = cluster_no, y = n, fill = x29_family_income)) +
  geom_bar(position="dodge", stat="identity", alpha = 0.8) +
  labs( x = "Clusters (1 and 4 are 'average')",
      y = "Total # of Fishers",
      title = "Ability to cover family costs", 
      fill = "Household funds are..", 
      caption = "Hypothesis: insufficient funds = less engaged") +
  geom_text(aes(label = n),position = position_dodge(0.9), vjust = 2, size = 2, color = "gray1")+
  theme_bw()

Below, Food Security is asked in terms of the quality of food availability the past year, and the confidence for the upcoming year.

Measuring Question 54, “How do you rate the last year in terms of food availability?”

x54_cluster_counts <- food_security %>% 
  group_by(cluster_no) %>% 
  count(x54_food_availability) %>% 
  drop_na()

ggplot(x54_cluster_counts,
       aes(x = cluster_no, y = n, fill = x54_food_availability)) +
  geom_bar(stat="identity", alpha = 0.8) +
  labs( x = "Clusters (1 and 4 are 'average')",
      y = "Total # of Fishers",
      title = "Food Availability (Past Year)", 
      fill = "'Rating' Food availability",
      caption = "How do you rate the last year in terms of food availability?") 

ggplot(x54_cluster_counts,
       aes(x = cluster_no, y = n, fill = x54_food_availability)) +
  geom_bar(position="fill", stat="identity", alpha = 0.7) +
  labs( x = "Fisher Type",
      y = "Percentage of fishers",
      title = "Food Availability (past Year)", 
      fill = "Level of Food") +
   scale_fill_viridis(discrete = T)

“Are you confident that you will be able to procure enough food for you and your family for the next 12 months?

x59_counts <- food_security %>% 
  group_by(cluster_no) %>% 
  count(x59_food_procurement) %>% 
  drop_na() %>% 
  mutate(x59_food_procurement = recode_factor(x59_food_procurement,
                                              "Confident not" = "Not confident",
                                              "Very confident not" = "Very not confident"))

ggplot(x59_counts,
       aes(x = cluster_no, y = n, fill = x59_food_procurement)) +
  geom_bar(stat="identity", alpha = 0.8) +
  labs( x = "Clusters (1 and 4 are 'average')",
      y = "Total # of Fishers",
      title = "Food Security Confidence (Upcoming Year)", 
      fill = "'Rating' Food availability",
      caption = "Are you confident that you will be able to procure enough food for you and your family for the next 12 months?") +
  # geom_text(aes(label = n),position = position_dodge(0.9), vjust = 2, size = 2, color = "gray1")+
  theme_bw()

Food Security (“worrying about food”)

x55_cluster_counts <- food_security %>% 
  group_by(x55_worry_food) %>% 
  count(cluster_no)

ggplot(x55_cluster_counts,
       aes(x = cluster_no, y = n, fill = x55_worry_food)) +
  geom_bar(position="fill", stat="identity", alpha = 0.7) +
  labs( x = "Fisher Type",
      y = "Percentage of fishers",
      title = "Financial Insecurity", 
      color = "Cluster", 
      caption = "Worry about Food Security (last 12 months") +
   scale_fill_viridis(discrete = T)

#8. Enforcement Type + Clusters

Starting with Enforcement

enforcement_df <- scaled_km_df %>% 
  select(eng_mean, agree_mean, cluster_no, x49_enforcement_responsible, x51b_fishers_reserves, country) %>% 
filter(x49_enforcement_responsible %in% c("Fisheries Management Body",
                                         "Subnational Government",
                                         "No enforcement system",
                                         "Myself",
                                         "National Government", na.rm = FALSE))

### To make a grouped bar chart, I want counts for each cluster


enforcement_counts <- enforcement_df %>% 
  select(eng_mean, agree_mean, cluster_no, x49_enforcement_responsible, x51b_fishers_reserves, country) %>% 
  group_by(x49_enforcement_responsible) %>% 
  count(cluster_no)

  
swtiched_enforcement_counts <- enforcement_df %>% 
  group_by(cluster_no) %>% 
  count(x49_enforcement_responsible)
  

### Data cleaning




ggplot(swtiched_enforcement_counts,
       aes(x = cluster_no, y = n, fill = x49_enforcement_responsible)) +
  geom_bar(position="dodge", stat="identity") +
  labs( x = "Cluster #",
      y = "Enforcement",
      title = "Enforcement By Cluster", 
      Fill = "Enforcement Type") +
    guides(colour = guide_legend(override.aes = list(size=7))) 

  # scale_x_continuous(limits = c(-3, 2),
  #                    breaks = c(-2, -1, 0, 1, 2)) +
  #  scale_y_continuous(limits = c(-1.5, 2),
  #                    breaks = c( -1, 0, 1, 2)) +



ggplot(enforcement_counts,
       aes(x = x49_enforcement_responsible, y = n, fill = cluster_no)) +
  geom_bar(position="dodge", stat="identity", alpha = 0.8) +
  labs( x = "Enforcement Responsibility",
      y = "Cluster",
      title = "Enforcement By Cluster", 
      color = "Cluster") +
  geom_text(aes(label = n),position = position_dodge(0.9), vjust = 1, size = 2, color = "gray1") +
    guides(colour = guide_legend(override.aes = list(size=7))) +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

“Who is responsible for enforcement in your community? 1. Fisheries Management Body 2. National Government 3. Subnational Government 4. Other (please state) 5. There is no enforcement system”

Chi-Squared Means Testing

**For the Figures that we include in the Manuscript, we’ll need to test the significance of the different groups. We have very large Ns and feel confident in the groupings for that reason.

2a. Enforcement Responsibility

Chi-Squared Test Null: There is no significant difference between the Cluster makeup of each bucket of enforcement responsibility Alternative: there is a menaingful difference between the compliance behaviors of each enforcement responsoibility

enforcement_counts_table <- enforcement_counts %>% 
   pivot_wider(names_from = x49_enforcement_responsible, values_from = n) %>% 
  rename(Cluster = cluster_no)

enforcement_counts_prop <- enforcement_counts_table %>% 
  janitor::adorn_percentages(denominator = "col") %>% 
  adorn_pct_formatting(digits = 0) %>% 
  adorn_ns(position = "front")


enforcement_counts_prop

##     Cluster Fisheries Management Body   Myself National Government
##   Committed                 633 (30%) 22 (19%)           192 (23%)
##   Resistant                 276 (13%) 27 (23%)           150 (18%)
##  Supportive                 681 (33%) 46 (39%)           277 (34%)
##    Wavering                 499 (24%) 23 (19%)           199 (24%)
##  No enforcement system Subnational Government
##                4  (2%)              123 (12%)
##              106 (55%)              107 (10%)
##               51 (26%)              435 (42%)
##               33 (17%)              361 (35%)

Nicer version of this table

kable(enforcement_counts_prop) %>% 
  kable_styling()

Cluster	Fisheries Management Body	Myself	National Government	No enforcement system	Subnational Government
Committed	633 (30%)	22 (19%)	192 (23%)	4 (2%)	123 (12%)
Resistant	276 (13%)	27 (23%)	150 (18%)	106 (55%)	107 (10%)
Supportive	681 (33%)	46 (39%)	277 (34%)	51 (26%)	435 (42%)
Wavering	499 (24%)	23 (19%)	199 (24%)	33 (17%)	361 (35%)

Now let’s run the Chi-Squared test.

enforcement_pre_chi <- enforcement_counts_table %>% 
  select(-Cluster)

enforcement_chi <- chisq.test(enforcement_pre_chi)


enforcement_chi

## 
##  Pearson's Chi-squared test
## 
## data:  enforcement_pre_chi
## X-squared = 431.86, df = 12, p-value < 2.2e-16

Clustered Enforcement has a p-value < 2.2e-16

2b. Perceived Catch

perceived_catch_table <- x19_cluster_counts %>% 
  pivot_wider(values_from =  n, names_from = x19_current_fish_catch) %>% 
  rename(Cluster = cluster_no) 

preceived_catch_props <- perceived_catch_table %>% 
  janitor::adorn_percentages(denominator = "col") %>% 
  adorn_pct_formatting(digits = 0) %>% 
  adorn_ns(position = "front")

kable(preceived_catch_props) %>% 
  kable_styling()

Cluster	Declined a lot	Declined slightly	Stayed the same	Improved slightly	Improved heavily
Committed	200 (23%)	422 (23%)	186 (16%)	151 (41%)	14 (52%)
Resistant	187 (22%)	350 (19%)	115 (10%)	70 (19%)	1 (4%)
Supportive	322 (37%)	632 (34%)	447 (38%)	95 (26%)	9 (33%)
Wavering	160 (18%)	452 (24%)	423 (36%)	56 (15%)	3 (11%)

Now let’s do the Chi-squared for Perceived Catch and Clusters

pre_chi_perceived_catch <- perceived_catch_table %>% 
  ungroup() %>%
  select(2:6)


chi_perceived_catch <- chisq.test(pre_chi_perceived_catch)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

chi_perceived_catch

## 
##  Pearson's Chi-squared test
## 
## data:  pre_chi_perceived_catch
## X-squared = 242.96, df = 12, p-value < 2.2e-16

Clusters and Perceived catch have a p-value < 2.2e-16

2c. Fishing Effort in Offseason